Overview

Dataset statistics

Number of variables22
Number of observations7823
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.1%
Total size in memory1.6 MiB
Average record size in memory217.8 B

Variable types

Numeric9
Categorical12
Boolean1

Alerts

Dataset has 10 (0.1%) duplicate rowsDuplicates
pdays is highly overall correlated with pmonths and 2 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
cons.price.idx is highly overall correlated with emp.var.rate and 3 other fieldsHigh correlation
cons.conf.idx is highly overall correlated with monthHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 3 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 3 other fieldsHigh correlation
pmonths is highly overall correlated with pdays and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
contact is highly overall correlated with cons.price.idx and 2 other fieldsHigh correlation
month is highly overall correlated with emp.var.rate and 5 other fieldsHigh correlation
previous is highly overall correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 3 other fieldsHigh correlation
pastEmail is highly overall correlated with poutcomeHigh correlation
default is highly imbalanced (54.4%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
previous is highly imbalanced (72.6%)Imbalance
poutcome is highly imbalanced (59.1%)Imbalance
pastEmail is highly imbalanced (69.6%)Imbalance
responded is highly imbalanced (51.0%)Imbalance

Reproduction

Analysis started2024-01-09 16:38:56.971307
Analysis finished2024-01-09 16:39:13.882789
Duration16.91 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

custAge
Real number (ℝ)

Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.518727
Minimum18
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:14.012331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q132
median38
Q346
95-th percentile57
Maximum71
Range53
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.7162571
Coefficient of variation (CV)0.24586463
Kurtosis-0.41152054
Mean39.518727
Median Absolute Deviation (MAD)7
Skewness0.50877095
Sum309155
Variance94.405652
MonotonicityNot monotonic
2024-01-09T22:09:14.224231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 399
 
5.1%
33 374
 
4.8%
32 369
 
4.7%
30 351
 
4.5%
35 331
 
4.2%
34 321
 
4.1%
36 319
 
4.1%
37 295
 
3.8%
29 279
 
3.6%
38 277
 
3.5%
Other values (44) 4508
57.6%
ValueCountFrequency (%)
18 5
 
0.1%
19 5
 
0.1%
20 12
 
0.2%
21 17
 
0.2%
22 33
 
0.4%
23 34
 
0.4%
24 93
1.2%
25 110
1.4%
26 132
1.7%
27 154
2.0%
ValueCountFrequency (%)
71 15
0.2%
70 14
0.2%
69 4
 
0.1%
68 4
 
0.1%
67 4
 
0.1%
66 7
0.1%
65 10
0.1%
64 13
0.2%
63 4
 
0.1%
62 14
0.2%

profession
Categorical

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
admin.
1991 
blue-collar
1788 
technician
1292 
services
761 
management
561 
Other values (7)
1430 

Length

Max length13
Median length12
Mean length8.9939921
Min length6

Characters and Unicode

Total characters70360
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowadmin.
3rd rowadmin.
4th rowblue-collar
5th rowentrepreneur

Common Values

ValueCountFrequency (%)
admin. 1991
25.5%
blue-collar 1788
22.9%
technician 1292
16.5%
services 761
 
9.7%
management 561
 
7.2%
entrepreneur 310
 
4.0%
self-employed 271
 
3.5%
retired 255
 
3.3%
housemaid 198
 
2.5%
unemployed 182
 
2.3%
Other values (2) 214
 
2.7%

Length

2024-01-09T22:09:14.441936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 1991
25.5%
blue-collar 1788
22.9%
technician 1292
16.5%
services 761
 
9.7%
management 561
 
7.2%
entrepreneur 310
 
4.0%
self-employed 271
 
3.5%
retired 255
 
3.3%
housemaid 198
 
2.5%
unemployed 182
 
2.3%
Other values (2) 214
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 9000
12.8%
n 6839
 
9.7%
a 6391
 
9.1%
l 6088
 
8.7%
i 5789
 
8.2%
c 5133
 
7.3%
r 3989
 
5.7%
m 3764
 
5.3%
d 3048
 
4.3%
t 2720
 
3.9%
Other values (14) 17599
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66310
94.2%
Dash Punctuation 2059
 
2.9%
Other Punctuation 1991
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9000
13.6%
n 6839
10.3%
a 6391
9.6%
l 6088
9.2%
i 5789
8.7%
c 5133
 
7.7%
r 3989
 
6.0%
m 3764
 
5.7%
d 3048
 
4.6%
t 2720
 
4.1%
Other values (12) 13549
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 2059
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66310
94.2%
Common 4050
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9000
13.6%
n 6839
10.3%
a 6391
9.6%
l 6088
9.2%
i 5789
8.7%
c 5133
 
7.7%
r 3989
 
6.0%
m 3764
 
5.7%
d 3048
 
4.6%
t 2720
 
4.1%
Other values (12) 13549
20.4%
Common
ValueCountFrequency (%)
- 2059
50.8%
. 1991
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9000
12.8%
n 6839
 
9.7%
a 6391
 
9.1%
l 6088
 
8.7%
i 5789
 
8.2%
c 5133
 
7.3%
r 3989
 
5.7%
m 3764
 
5.3%
d 3048
 
4.3%
t 2720
 
3.9%
Other values (14) 17599
25.0%

marital
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
married
4692 
single
2234 
divorced
887 
unknown
 
10

Length

Max length8
Median length7
Mean length6.8278154
Min length6

Characters and Unicode

Total characters53414
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowsingle
3rd rowdivorced
4th rowsingle
5th rowmarried

Common Values

ValueCountFrequency (%)
married 4692
60.0%
single 2234
28.6%
divorced 887
 
11.3%
unknown 10
 
0.1%

Length

2024-01-09T22:09:14.573935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:14.767031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
married 4692
60.0%
single 2234
28.6%
divorced 887
 
11.3%
unknown 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 10271
19.2%
i 7813
14.6%
e 7813
14.6%
d 6466
12.1%
m 4692
8.8%
a 4692
8.8%
n 2264
 
4.2%
s 2234
 
4.2%
g 2234
 
4.2%
l 2234
 
4.2%
Other values (6) 2701
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53414
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10271
19.2%
i 7813
14.6%
e 7813
14.6%
d 6466
12.1%
m 4692
8.8%
a 4692
8.8%
n 2264
 
4.2%
s 2234
 
4.2%
g 2234
 
4.2%
l 2234
 
4.2%
Other values (6) 2701
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 53414
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10271
19.2%
i 7813
14.6%
e 7813
14.6%
d 6466
12.1%
m 4692
8.8%
a 4692
8.8%
n 2264
 
4.2%
s 2234
 
4.2%
g 2234
 
4.2%
l 2234
 
4.2%
Other values (6) 2701
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10271
19.2%
i 7813
14.6%
e 7813
14.6%
d 6466
12.1%
m 4692
8.8%
a 4692
8.8%
n 2264
 
4.2%
s 2234
 
4.2%
g 2234
 
4.2%
l 2234
 
4.2%
Other values (6) 2701
 
5.1%

schooling
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
university.degree
2294 
high.school
1781 
basic.9y
1200 
professional.course
985 
basic.4y
782 
Other values (3)
781 

Length

Max length19
Median length17
Mean length12.66036
Min length7

Characters and Unicode

Total characters99042
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowuniversity.degree
2nd rowhigh.school
3rd rowuniversity.degree
4th rowuniversity.degree
5th rowuniversity.degree

Common Values

ValueCountFrequency (%)
university.degree 2294
29.3%
high.school 1781
22.8%
basic.9y 1200
15.3%
professional.course 985
12.6%
basic.4y 782
 
10.0%
basic.6y 412
 
5.3%
unknown 368
 
4.7%
illiterate 1
 
< 0.1%

Length

2024-01-09T22:09:14.949989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:15.139558image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 2294
29.3%
high.school 1781
22.8%
basic.9y 1200
15.3%
professional.course 985
12.6%
basic.4y 782
 
10.0%
basic.6y 412
 
5.3%
unknown 368
 
4.7%
illiterate 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 11148
 
11.3%
i 9750
 
9.8%
s 9424
 
9.5%
. 7454
 
7.5%
o 6885
 
7.0%
r 6559
 
6.6%
h 5343
 
5.4%
c 5160
 
5.2%
y 4688
 
4.7%
n 4383
 
4.4%
Other values (15) 28248
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 89194
90.1%
Other Punctuation 7454
 
7.5%
Decimal Number 2394
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11148
12.5%
i 9750
10.9%
s 9424
10.6%
o 6885
 
7.7%
r 6559
 
7.4%
h 5343
 
6.0%
c 5160
 
5.8%
y 4688
 
5.3%
n 4383
 
4.9%
g 4075
 
4.6%
Other values (11) 21779
24.4%
Decimal Number
ValueCountFrequency (%)
9 1200
50.1%
4 782
32.7%
6 412
 
17.2%
Other Punctuation
ValueCountFrequency (%)
. 7454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89194
90.1%
Common 9848
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11148
12.5%
i 9750
10.9%
s 9424
10.6%
o 6885
 
7.7%
r 6559
 
7.4%
h 5343
 
6.0%
c 5160
 
5.8%
y 4688
 
5.3%
n 4383
 
4.9%
g 4075
 
4.6%
Other values (11) 21779
24.4%
Common
ValueCountFrequency (%)
. 7454
75.7%
9 1200
 
12.2%
4 782
 
7.9%
6 412
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11148
 
11.3%
i 9750
 
9.8%
s 9424
 
9.5%
. 7454
 
7.5%
o 6885
 
7.0%
r 6559
 
6.6%
h 5343
 
5.4%
c 5160
 
5.2%
y 4688
 
4.7%
n 4383
 
4.4%
Other values (15) 28248
28.5%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
no
6260 
unknown
1562 
yes
 
1

Length

Max length7
Median length2
Mean length2.9984661
Min length2

Characters and Unicode

Total characters23457
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowno
2nd rowno
3rd rowunknown
4th rowunknown
5th rowno

Common Values

ValueCountFrequency (%)
no 6260
80.0%
unknown 1562
 
20.0%
yes 1
 
< 0.1%

Length

2024-01-09T22:09:15.360542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:15.506468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 6260
80.0%
unknown 1562
 
20.0%
yes 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 10946
46.7%
o 7822
33.3%
u 1562
 
6.7%
k 1562
 
6.7%
w 1562
 
6.7%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23457
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10946
46.7%
o 7822
33.3%
u 1562
 
6.7%
k 1562
 
6.7%
w 1562
 
6.7%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 23457
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10946
46.7%
o 7822
33.3%
u 1562
 
6.7%
k 1562
 
6.7%
w 1562
 
6.7%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10946
46.7%
o 7822
33.3%
u 1562
 
6.7%
k 1562
 
6.7%
w 1562
 
6.7%
y 1
 
< 0.1%
e 1
 
< 0.1%
s 1
 
< 0.1%

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
yes
4084 
no
3565 
unknown
 
174

Length

Max length7
Median length3
Mean length2.6332609
Min length2

Characters and Unicode

Total characters20600
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes 4084
52.2%
no 3565
45.6%
unknown 174
 
2.2%

Length

2024-01-09T22:09:15.660790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:15.820778image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 4084
52.2%
no 3565
45.6%
unknown 174
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 4087
19.8%
y 4084
19.8%
e 4084
19.8%
s 4084
19.8%
o 3739
18.2%
u 174
 
0.8%
k 174
 
0.8%
w 174
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20600
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4087
19.8%
y 4084
19.8%
e 4084
19.8%
s 4084
19.8%
o 3739
18.2%
u 174
 
0.8%
k 174
 
0.8%
w 174
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 20600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4087
19.8%
y 4084
19.8%
e 4084
19.8%
s 4084
19.8%
o 3739
18.2%
u 174
 
0.8%
k 174
 
0.8%
w 174
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4087
19.8%
y 4084
19.8%
e 4084
19.8%
s 4084
19.8%
o 3739
18.2%
u 174
 
0.8%
k 174
 
0.8%
w 174
 
0.8%

loan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
no
6430 
yes
1219 
unknown
 
174

Length

Max length7
Median length2
Mean length2.2670331
Min length2

Characters and Unicode

Total characters17735
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 6430
82.2%
yes 1219
 
15.6%
unknown 174
 
2.2%

Length

2024-01-09T22:09:15.946393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:16.141469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 6430
82.2%
yes 1219
 
15.6%
unknown 174
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 6952
39.2%
o 6604
37.2%
y 1219
 
6.9%
e 1219
 
6.9%
s 1219
 
6.9%
u 174
 
1.0%
k 174
 
1.0%
w 174
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17735
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6952
39.2%
o 6604
37.2%
y 1219
 
6.9%
e 1219
 
6.9%
s 1219
 
6.9%
u 174
 
1.0%
k 174
 
1.0%
w 174
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17735
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 6952
39.2%
o 6604
37.2%
y 1219
 
6.9%
e 1219
 
6.9%
s 1219
 
6.9%
u 174
 
1.0%
k 174
 
1.0%
w 174
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 6952
39.2%
o 6604
37.2%
y 1219
 
6.9%
e 1219
 
6.9%
s 1219
 
6.9%
u 174
 
1.0%
k 174
 
1.0%
w 174
 
1.0%

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
cellular
4948 
telephone
2875 

Length

Max length9
Median length8
Mean length8.3675061
Min length8

Characters and Unicode

Total characters65459
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowtelephone
3rd rowcellular
4th rowcellular
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 4948
63.2%
telephone 2875
36.8%

Length

2024-01-09T22:09:16.311383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:16.527381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
cellular 4948
63.2%
telephone 2875
36.8%

Most occurring characters

ValueCountFrequency (%)
l 17719
27.1%
e 13573
20.7%
c 4948
 
7.6%
u 4948
 
7.6%
a 4948
 
7.6%
r 4948
 
7.6%
t 2875
 
4.4%
p 2875
 
4.4%
h 2875
 
4.4%
o 2875
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65459
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 17719
27.1%
e 13573
20.7%
c 4948
 
7.6%
u 4948
 
7.6%
a 4948
 
7.6%
r 4948
 
7.6%
t 2875
 
4.4%
p 2875
 
4.4%
h 2875
 
4.4%
o 2875
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 65459
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 17719
27.1%
e 13573
20.7%
c 4948
 
7.6%
u 4948
 
7.6%
a 4948
 
7.6%
r 4948
 
7.6%
t 2875
 
4.4%
p 2875
 
4.4%
h 2875
 
4.4%
o 2875
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 17719
27.1%
e 13573
20.7%
c 4948
 
7.6%
u 4948
 
7.6%
a 4948
 
7.6%
r 4948
 
7.6%
t 2875
 
4.4%
p 2875
 
4.4%
h 2875
 
4.4%
o 2875
 
4.4%

month
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
may
2740 
jul
1271 
aug
1167 
jun
992 
nov
775 
Other values (5)
878 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23469
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapr
2nd rowjun
3rd rowjul
4th rowjul
5th rowjun

Common Values

ValueCountFrequency (%)
may 2740
35.0%
jul 1271
16.2%
aug 1167
14.9%
jun 992
 
12.7%
nov 775
 
9.9%
apr 526
 
6.7%
oct 133
 
1.7%
sep 99
 
1.3%
mar 97
 
1.2%
dec 23
 
0.3%

Length

2024-01-09T22:09:16.663722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:16.877682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
may 2740
35.0%
jul 1271
16.2%
aug 1167
14.9%
jun 992
 
12.7%
nov 775
 
9.9%
apr 526
 
6.7%
oct 133
 
1.7%
sep 99
 
1.3%
mar 97
 
1.2%
dec 23
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 4530
19.3%
u 3430
14.6%
m 2837
12.1%
y 2740
11.7%
j 2263
9.6%
n 1767
 
7.5%
l 1271
 
5.4%
g 1167
 
5.0%
o 908
 
3.9%
v 775
 
3.3%
Other values (7) 1781
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23469
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4530
19.3%
u 3430
14.6%
m 2837
12.1%
y 2740
11.7%
j 2263
9.6%
n 1767
 
7.5%
l 1271
 
5.4%
g 1167
 
5.0%
o 908
 
3.9%
v 775
 
3.3%
Other values (7) 1781
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 23469
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4530
19.3%
u 3430
14.6%
m 2837
12.1%
y 2740
11.7%
j 2263
9.6%
n 1767
 
7.5%
l 1271
 
5.4%
g 1167
 
5.0%
o 908
 
3.9%
v 775
 
3.3%
Other values (7) 1781
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4530
19.3%
u 3430
14.6%
m 2837
12.1%
y 2740
11.7%
j 2263
9.6%
n 1767
 
7.5%
l 1271
 
5.4%
g 1167
 
5.0%
o 908
 
3.9%
v 775
 
3.3%
Other values (7) 1781
 
7.6%

day_of_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
mon
1690 
thu
1582 
wed
1561 
tue
1552 
fri
1438 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23469
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwed
2nd rowthu
3rd rowtue
4th rowtue
5th rowthu

Common Values

ValueCountFrequency (%)
mon 1690
21.6%
thu 1582
20.2%
wed 1561
20.0%
tue 1552
19.8%
fri 1438
18.4%

Length

2024-01-09T22:09:17.112260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:17.332930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
mon 1690
21.6%
thu 1582
20.2%
wed 1561
20.0%
tue 1552
19.8%
fri 1438
18.4%

Most occurring characters

ValueCountFrequency (%)
t 3134
13.4%
u 3134
13.4%
e 3113
13.3%
m 1690
7.2%
o 1690
7.2%
n 1690
7.2%
h 1582
6.7%
w 1561
6.7%
d 1561
6.7%
f 1438
6.1%
Other values (2) 2876
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23469
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3134
13.4%
u 3134
13.4%
e 3113
13.3%
m 1690
7.2%
o 1690
7.2%
n 1690
7.2%
h 1582
6.7%
w 1561
6.7%
d 1561
6.7%
f 1438
6.1%
Other values (2) 2876
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 23469
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3134
13.4%
u 3134
13.4%
e 3113
13.3%
m 1690
7.2%
o 1690
7.2%
n 1690
7.2%
h 1582
6.7%
w 1561
6.7%
d 1561
6.7%
f 1438
6.1%
Other values (2) 2876
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3134
13.4%
u 3134
13.4%
e 3113
13.3%
m 1690
7.2%
o 1690
7.2%
n 1690
7.2%
h 1582
6.7%
w 1561
6.7%
d 1561
6.7%
f 1438
6.1%
Other values (2) 2876
12.3%

campaign
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2676722
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:17.505927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7119123
Coefficient of variation (CV)0.7549205
Kurtosis4.208349
Mean2.2676722
Median Absolute Deviation (MAD)1
Skewness1.9550904
Sum17740
Variance2.9306436
MonotonicityNot monotonic
2024-01-09T22:09:17.642015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 3404
43.5%
2 2097
26.8%
3 1021
 
13.1%
4 531
 
6.8%
5 282
 
3.6%
6 200
 
2.6%
7 108
 
1.4%
8 71
 
0.9%
9 69
 
0.9%
10 40
 
0.5%
ValueCountFrequency (%)
1 3404
43.5%
2 2097
26.8%
3 1021
 
13.1%
4 531
 
6.8%
5 282
 
3.6%
6 200
 
2.6%
7 108
 
1.4%
8 71
 
0.9%
9 69
 
0.9%
10 40
 
0.5%
ValueCountFrequency (%)
10 40
 
0.5%
9 69
 
0.9%
8 71
 
0.9%
7 108
 
1.4%
6 200
 
2.6%
5 282
 
3.6%
4 531
 
6.8%
3 1021
 
13.1%
2 2097
26.8%
1 3404
43.5%

pdays
Real number (ℝ)

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean970.1911
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:17.759012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation166.66225
Coefficient of variation (CV)0.17178291
Kurtosis29.514124
Mean970.1911
Median Absolute Deviation (MAD)0
Skewness-5.6130244
Sum7589805
Variance27776.306
MonotonicityNot monotonic
2024-01-09T22:09:17.878014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
999 7596
97.1%
6 62
 
0.8%
3 56
 
0.7%
4 20
 
0.3%
7 12
 
0.2%
9 11
 
0.1%
2 11
 
0.1%
12 10
 
0.1%
5 7
 
0.1%
10 7
 
0.1%
Other values (10) 31
 
0.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4
 
0.1%
2 11
 
0.1%
3 56
0.7%
4 20
 
0.3%
5 7
 
0.1%
6 62
0.8%
7 12
 
0.2%
8 3
 
< 0.1%
9 11
 
0.1%
ValueCountFrequency (%)
999 7596
97.1%
22 1
 
< 0.1%
17 2
 
< 0.1%
16 4
 
0.1%
15 5
 
0.1%
14 4
 
0.1%
13 3
 
< 0.1%
12 10
 
0.1%
11 3
 
< 0.1%
10 7
 
0.1%

previous
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
0
6818 
1
880 
2
 
100
3
 
19
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7823
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

Length

2024-01-09T22:09:18.002972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:18.136020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7823
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6818
87.2%
1 880
 
11.2%
2 100
 
1.3%
3 19
 
0.2%
4 6
 
0.1%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
nonexistent
6818 
failure
792 
success
 
213

Length

Max length11
Median length11
Mean length10.486131
Min length7

Characters and Unicode

Total characters82033
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 6818
87.2%
failure 792
 
10.1%
success 213
 
2.7%

Length

2024-01-09T22:09:18.266010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:18.424115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 6818
87.2%
failure 792
 
10.1%
success 213
 
2.7%

Most occurring characters

ValueCountFrequency (%)
n 20454
24.9%
e 14641
17.8%
t 13636
16.6%
i 7610
 
9.3%
s 7457
 
9.1%
o 6818
 
8.3%
x 6818
 
8.3%
u 1005
 
1.2%
f 792
 
1.0%
a 792
 
1.0%
Other values (3) 2010
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82033
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 20454
24.9%
e 14641
17.8%
t 13636
16.6%
i 7610
 
9.3%
s 7457
 
9.1%
o 6818
 
8.3%
x 6818
 
8.3%
u 1005
 
1.2%
f 792
 
1.0%
a 792
 
1.0%
Other values (3) 2010
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 82033
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 20454
24.9%
e 14641
17.8%
t 13636
16.6%
i 7610
 
9.3%
s 7457
 
9.1%
o 6818
 
8.3%
x 6818
 
8.3%
u 1005
 
1.2%
f 792
 
1.0%
a 792
 
1.0%
Other values (3) 2010
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 20454
24.9%
e 14641
17.8%
t 13636
16.6%
i 7610
 
9.3%
s 7457
 
9.1%
o 6818
 
8.3%
x 6818
 
8.3%
u 1005
 
1.2%
f 792
 
1.0%
a 792
 
1.0%
Other values (3) 2010
 
2.5%

emp.var.rate
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082704845
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative3297
Negative (%)42.1%
Memory size380.3 KiB
2024-01-09T22:09:18.540113image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5512184
Coefficient of variation (CV)18.756076
Kurtosis-1.1034964
Mean0.082704845
Median Absolute Deviation (MAD)0.3
Skewness-0.70022828
Sum647
Variance2.4062784
MonotonicityNot monotonic
2024-01-09T22:09:18.762665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 3010
38.5%
-1.8 1847
23.6%
1.1 1516
19.4%
-0.1 725
 
9.3%
-2.9 291
 
3.7%
-3.4 173
 
2.2%
-1.7 129
 
1.6%
-1.1 109
 
1.4%
-3 22
 
0.3%
-0.2 1
 
< 0.1%
ValueCountFrequency (%)
-3.4 173
 
2.2%
-3 22
 
0.3%
-2.9 291
 
3.7%
-1.8 1847
23.6%
-1.7 129
 
1.6%
-1.1 109
 
1.4%
-0.2 1
 
< 0.1%
-0.1 725
 
9.3%
1.1 1516
19.4%
1.4 3010
38.5%
ValueCountFrequency (%)
1.4 3010
38.5%
1.1 1516
19.4%
-0.1 725
 
9.3%
-0.2 1
 
< 0.1%
-1.1 109
 
1.4%
-1.7 129
 
1.6%
-1.8 1847
23.6%
-2.9 291
 
3.7%
-3 22
 
0.3%
-3.4 173
 
2.2%

cons.price.idx
Real number (ℝ)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.567327
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:18.925705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.843
Q193.075
median93.444
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57218168
Coefficient of variation (CV)0.0061151868
Kurtosis-0.8832206
Mean93.567327
Median Absolute Deviation (MAD)0.55
Skewness-0.16832232
Sum731977.2
Variance0.32739188
MonotonicityNot monotonic
2024-01-09T22:09:19.112594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 1516
19.4%
92.893 1197
15.3%
93.918 1184
15.1%
93.444 1008
12.9%
94.465 818
10.5%
93.2 706
9.0%
93.075 500
 
6.4%
92.963 135
 
1.7%
92.201 124
 
1.6%
92.431 85
 
1.1%
Other values (16) 550
 
7.0%
ValueCountFrequency (%)
92.201 124
 
1.6%
92.379 42
 
0.5%
92.431 85
 
1.1%
92.469 32
 
0.4%
92.649 46
 
0.6%
92.713 22
 
0.3%
92.756 1
 
< 0.1%
92.843 52
 
0.7%
92.893 1197
15.3%
92.963 135
 
1.7%
ValueCountFrequency (%)
94.767 23
 
0.3%
94.601 29
 
0.4%
94.465 818
10.5%
94.215 55
 
0.7%
94.199 57
 
0.7%
94.055 39
 
0.5%
94.027 35
 
0.4%
93.994 1516
19.4%
93.918 1184
15.1%
93.876 27
 
0.3%

cons.conf.idx
Real number (ℝ)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.618561
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative7823
Negative (%)100.0%
Memory size380.3 KiB
2024-01-09T22:09:19.336257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-34.8
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.5983809
Coefficient of variation (CV)-0.11320886
Kurtosis-0.39993174
Mean-40.618561
Median Absolute Deviation (MAD)4.4
Skewness0.29171563
Sum-317759
Variance21.145107
MonotonicityNot monotonic
2024-01-09T22:09:19.535815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 1516
19.4%
-46.2 1197
15.3%
-42.7 1184
15.1%
-36.1 1008
12.9%
-41.8 818
10.5%
-42 706
9.0%
-47.1 500
 
6.4%
-40.8 135
 
1.7%
-31.4 124
 
1.6%
-26.9 85
 
1.1%
Other values (16) 550
 
7.0%
ValueCountFrequency (%)
-50.8 23
 
0.3%
-50 52
 
0.7%
-49.5 29
 
0.4%
-47.1 500
6.4%
-46.2 1197
15.3%
-45.9 1
 
< 0.1%
-42.7 1184
15.1%
-42 706
9.0%
-41.8 818
10.5%
-40.8 135
 
1.7%
ValueCountFrequency (%)
-26.9 85
 
1.1%
-29.8 42
 
0.5%
-30.1 46
 
0.6%
-31.4 124
 
1.6%
-33 22
 
0.3%
-33.6 32
 
0.4%
-34.6 26
 
0.3%
-34.8 45
 
0.6%
-36.1 1008
12.9%
-36.4 1516
19.4%

euribor3m
Real number (ℝ)

Distinct274
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6242086
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:19.766855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.8513
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7219328
Coefficient of variation (CV)0.47511967
Kurtosis-1.4183074
Mean3.6242086
Median Absolute Deviation (MAD)0.108
Skewness-0.70184481
Sum28352.184
Variance2.9650526
MonotonicityNot monotonic
2024-01-09T22:09:19.969192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 590
 
7.5%
4.963 467
 
6.0%
4.962 454
 
5.8%
4.961 363
 
4.6%
4.964 231
 
3.0%
4.856 219
 
2.8%
1.405 219
 
2.8%
4.864 203
 
2.6%
4.96 195
 
2.5%
4.965 188
 
2.4%
Other values (264) 4694
60.0%
ValueCountFrequency (%)
0.634 2
 
< 0.1%
0.635 8
0.1%
0.636 5
0.1%
0.637 3
 
< 0.1%
0.638 3
 
< 0.1%
0.639 2
 
< 0.1%
0.64 2
 
< 0.1%
0.642 7
0.1%
0.643 1
 
< 0.1%
0.644 3
 
< 0.1%
ValueCountFrequency (%)
5.045 4
 
0.1%
5 2
 
< 0.1%
4.97 43
 
0.5%
4.968 184
 
2.4%
4.967 126
 
1.6%
4.966 131
 
1.7%
4.965 188
2.4%
4.964 231
3.0%
4.963 467
6.0%
4.962 454
5.8%

nr.employed
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.853
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:20.134028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation70.086254
Coefficient of variation (CV)0.013561967
Kurtosis0.048818915
Mean5167.853
Median Absolute Deviation (MAD)37.1
Skewness-1.034103
Sum40428114
Variance4912.083
MonotonicityNot monotonic
2024-01-09T22:09:20.769150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 3010
38.5%
5099.1 1749
22.4%
5191 1516
19.4%
5195.8 725
 
9.3%
5076.2 291
 
3.7%
5017.5 173
 
2.2%
4991.6 129
 
1.6%
4963.6 109
 
1.4%
5008.7 98
 
1.3%
5023.5 22
 
0.3%
ValueCountFrequency (%)
4963.6 109
 
1.4%
4991.6 129
 
1.6%
5008.7 98
 
1.3%
5017.5 173
 
2.2%
5023.5 22
 
0.3%
5076.2 291
 
3.7%
5099.1 1749
22.4%
5176.3 1
 
< 0.1%
5191 1516
19.4%
5195.8 725
9.3%
ValueCountFrequency (%)
5228.1 3010
38.5%
5195.8 725
 
9.3%
5191 1516
19.4%
5176.3 1
 
< 0.1%
5099.1 1749
22.4%
5076.2 291
 
3.7%
5023.5 22
 
0.3%
5017.5 173
 
2.2%
5008.7 98
 
1.3%
4991.6 129
 
1.6%

pmonths
Real number (ℝ)

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean970.01799
Minimum0
Maximum999
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size380.3 KiB
2024-01-09T22:09:20.960232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation167.66248
Coefficient of variation (CV)0.17284471
Kurtosis29.512067
Mean970.01799
Median Absolute Deviation (MAD)0
Skewness-5.6128889
Sum7588450.7
Variance28110.707
MonotonicityNot monotonic
2024-01-09T22:09:21.114587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
999 7596
97.1%
0.2 62
 
0.8%
0.1 56
 
0.7%
0.1333333333 20
 
0.3%
0.2333333333 12
 
0.2%
0.3 11
 
0.1%
0.06666666667 11
 
0.1%
0.4 10
 
0.1%
0.1666666667 7
 
0.1%
0.3333333333 7
 
0.1%
Other values (10) 31
 
0.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.03333333333 4
 
0.1%
0.06666666667 11
 
0.1%
0.1 56
0.7%
0.1333333333 20
 
0.3%
0.1666666667 7
 
0.1%
0.2 62
0.8%
0.2333333333 12
 
0.2%
0.2666666667 3
 
< 0.1%
0.3 11
 
0.1%
ValueCountFrequency (%)
999 7596
97.1%
0.7333333333 1
 
< 0.1%
0.5666666667 2
 
< 0.1%
0.5333333333 4
 
0.1%
0.5 5
 
0.1%
0.4666666667 4
 
0.1%
0.4333333333 3
 
< 0.1%
0.4 10
 
0.1%
0.3666666667 3
 
< 0.1%
0.3333333333 7
 
0.1%

pastEmail
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size380.3 KiB
0
6969 
2
 
311
1
 
248
3
 
176
4
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7823
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

Length

2024-01-09T22:09:21.241592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T22:09:21.435869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7823
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 7823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6969
89.1%
2 311
 
4.0%
1 248
 
3.2%
3 176
 
2.2%
4 119
 
1.5%

responded
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size326.8 KiB
False
6989 
True
834 
ValueCountFrequency (%)
False 6989
89.3%
True 834
 
10.7%
2024-01-09T22:09:21.595881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Interactions

2024-01-09T22:09:11.694254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:08:59.532444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.989464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.419060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.822928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.360844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.748673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:08.572173image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.162668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:11.853302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:08:59.665445image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.137394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.563058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.987777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.532077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.938607image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:08.823548image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.299760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.028799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:08:59.821075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.322909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.765745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:04.181190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.699073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:07.067366image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:08.981730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.519823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.203713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.003224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.438913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.918382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:04.329935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.834074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:07.212308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.136737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.670836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.354802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.193986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.578613image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.056382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:04.561292image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.965908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:07.345273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.273715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.809818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.511816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.365205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.735036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.225480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:04.711054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.110812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:07.484992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.397628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:10.945772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.635808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.495301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:01.904400image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.375389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:04.875397image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.253904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:07.708930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.562994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:11.114718image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.790959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.661310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.081323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.544449image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.046899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.412761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:08.254869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.761087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:11.289784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:12.966960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:00.791301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:02.224055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:03.673981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:05.196842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:06.584758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:08.431973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:09.973771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-01-09T22:09:11.471226image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-01-09T22:09:21.755863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
custAgecampaignpdaysemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthsprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekpreviouspoutcomepastEmailresponded
custAge1.0000.0060.0140.0570.0490.0930.0690.0570.0140.1600.1980.0650.1130.0000.0120.0800.0730.0200.0570.0870.0410.162
campaign0.0061.0000.0340.1250.081-0.0060.1030.1140.0340.0090.0180.0000.0000.0000.0030.0640.0460.0380.0270.0480.0210.055
pdays0.0140.0341.0000.2080.083-0.0670.2470.2531.0000.1130.0470.0180.0690.0200.0200.1040.2470.0330.5260.9680.4070.307
emp.var.rate0.0570.1250.2081.0000.6800.2980.9410.9450.2080.1140.0630.0460.1450.0380.0040.4600.6660.0250.2660.3610.2150.330
cons.price.idx0.0490.0810.0830.6801.0000.3060.5160.4870.0830.1190.0690.0650.1420.0670.0190.6800.6750.0420.2730.3660.2120.303
cons.conf.idx0.093-0.006-0.0670.2980.3061.0000.3090.200-0.0670.0950.0640.0430.1240.0340.0130.4180.5980.0330.2480.3390.1950.355
euribor3m0.0690.1030.2470.9410.5160.3091.0000.9270.2470.1250.0640.0490.1480.0370.0080.4700.6540.1270.2670.3930.2210.373
nr.employed0.0570.1140.2530.9450.4870.2000.9271.0000.2530.1120.0650.0490.1270.0210.0070.5060.6070.0370.2750.3750.2060.384
pmonths0.0140.0341.0000.2080.083-0.0670.2470.2531.0000.1130.0470.0180.0690.0200.0200.1040.2470.0330.5260.9680.4070.307
profession0.1600.0090.1130.1140.1190.0950.1250.1120.1131.0000.1790.2570.1440.0000.0120.1200.1030.0220.0560.0910.0420.132
marital0.1980.0180.0470.0630.0690.0640.0640.0650.0470.1791.0000.0800.0900.0170.0150.0500.0450.0080.0110.0340.0050.058
schooling0.0650.0000.0180.0460.0650.0430.0490.0490.0180.2570.0801.0000.1060.0000.0000.0890.0660.0240.0190.0260.0350.067
default0.1130.0000.0690.1450.1420.1240.1480.1270.0690.1440.0900.1061.0000.0060.0000.1220.1030.0000.0670.0720.0550.089
housing0.0000.0000.0200.0380.0670.0340.0370.0210.0200.0000.0170.0000.0061.0000.7080.0720.0530.0210.0090.0140.0110.000
loan0.0120.0030.0200.0040.0190.0130.0080.0070.0200.0120.0150.0000.0000.7081.0000.0000.0180.0190.0050.0090.0120.006
contact0.0800.0640.1040.4600.6800.4180.4700.5060.1040.1200.0500.0890.1220.0720.0001.0000.6050.0390.2260.2260.2080.140
month0.0730.0460.2470.6660.6750.5980.6540.6070.2470.1030.0450.0660.1030.0530.0180.6051.0000.0630.1530.2380.1260.287
day_of_week0.0200.0380.0330.0250.0420.0330.1270.0370.0330.0220.0080.0240.0000.0210.0190.0390.0631.0000.0070.0210.0000.024
previous0.0570.0270.5260.2660.2730.2480.2670.2750.5260.0560.0110.0190.0670.0090.0050.2260.1530.0071.0000.7220.4980.189
poutcome0.0870.0480.9680.3610.3660.3390.3930.3750.9680.0910.0340.0260.0720.0140.0090.2260.2380.0210.7221.0000.6460.302
pastEmail0.0410.0210.4070.2150.2120.1950.2210.2060.4070.0420.0050.0350.0550.0110.0120.2080.1260.0000.4980.6461.0000.129
responded0.1620.0550.3070.3300.3030.3550.3730.3840.3070.1320.0580.0670.0890.0000.0060.1400.2870.0240.1890.3020.1291.000

Missing values

2024-01-09T22:09:13.242038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-09T22:09:13.641872image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailresponded
034.0admin.singleuniversity.degreenonoyescellularaprwed29990nonexistent-1.893.075-47.11.4985099.1999.00no
231.0admin.singlehigh.schoolnononotelephonejunthu19990nonexistent1.494.465-41.84.9615228.1999.00no
352.0admin.divorceduniversity.degreeunknownyesnocellularjultue29990nonexistent1.493.918-42.74.9625228.1999.00no
439.0blue-collarsingleuniversity.degreeunknownyesnocellularjultue69990nonexistent1.493.918-42.74.9615228.1999.00no
540.0entrepreneurmarrieduniversity.degreenoyesnotelephonejunthu39990nonexistent1.494.465-41.84.8665228.1999.00no
650.0techniciansingleuniversity.degreenononocellularjultue39990nonexistent1.493.918-42.74.9615228.1999.00no
741.0technicianmarriedprofessional.coursenononocellularoctthu29990nonexistent-3.492.431-26.90.7415017.5999.00no
823.0blue-collarsinglebasic.4ynoyesnotelephonejunfri109990nonexistent1.494.465-41.84.9595228.1999.00no
929.0technicianmarriedprofessional.coursenoyesnocellularaugmon39990nonexistent1.493.444-36.14.9655228.1999.00no
1057.0retiredmarriedprofessional.courseunknownnonotelephonejunmon29990nonexistent1.494.465-41.84.9615228.1999.00no
custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailresponded
822530.0studentsinglebasic.9ynonoyestelephonemartue132success-1.893.369-34.80.6375008.70.1000002yes
822630.0technicianmarriedprofessional.coursenoyesnocellularocttue19990nonexistent-3.492.431-26.90.7375017.5999.0000000yes
822745.0blue-collarmarriedprofessional.coursenononocellularaugfri29990nonexistent1.493.444-36.14.9665228.1999.0000000yes
822839.0admin.marriedbasic.6yunknownnonocellularjulwed19990nonexistent1.493.918-42.74.9635228.1999.0000000yes
823028.0admin.marrieduniversity.degreenononocellularaprthu122success-1.893.075-47.11.3655099.10.0666670yes
823134.0admin.singleuniversity.degreenoyesnocellularaugwed19990nonexistent1.493.444-36.14.9655228.1999.0000000yes
823252.0servicesmarriedhigh.schoolunknownyesnocellularjulfri39990nonexistent1.493.918-42.74.9625228.1999.0000000yes
823355.0retiredmarriedhigh.schoolnoyesnocellularoctthu271success-3.492.431-26.90.7225017.50.2333330yes
823532.0self-employedsingleuniversity.degreenononocellularaprthu19990nonexistent-1.893.075-47.11.4355099.1999.0000000yes
823632.0housemaidmarrieduniversity.degreenononocellularjuntue19990nonexistent-2.992.963-40.81.0995076.2999.0000000yes

Duplicate rows

Most frequently occurring

custAgeprofessionmaritalschoolingdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedpmonthspastEmailresponded# duplicates
025.0admin.singlebasic.9ynononocellularjulmon29990nonexistent1.493.918-42.74.9605228.1999.00no2
127.0admin.singlehigh.schoolnononocellularjultue19990nonexistent1.493.918-42.74.9625228.1999.00no2
228.0blue-collarmarriedhigh.schoolunknownnonocellularjultue19990nonexistent1.493.918-42.74.9625228.1999.00no2
331.0blue-collarsinglebasic.9ynoyesnotelephonemaytue19990nonexistent1.193.994-36.44.8575191.0999.00no2
431.0managementsingleuniversity.degreenoyesnocellularnovthu19990nonexistent-0.193.200-42.04.0765195.8999.00no2
533.0blue-collarmarriedbasic.9ynoyesnotelephonemaywed19990nonexistent1.193.994-36.44.8575191.0999.00no2
634.0admin.singleuniversity.degreenononocellularjultue19990nonexistent1.493.918-42.74.9615228.1999.00no2
734.0blue-collarmarriedbasic.9yunknownyesnotelephonejunmon19990nonexistent1.494.465-41.84.8655228.1999.00no2
840.0techniciansingleprofessional.coursenoyesnocellularaugwed19990nonexistent1.493.444-36.14.9645228.1999.00no2
950.0self-employedmarrieduniversity.degreenoyesnocellularaugtue19990nonexistent1.493.444-36.14.9665228.1999.00no2